Adaptive quantum multi-objective parrot optimizer for task allocation of multi-UUV systems in underwater search

This paper presents a novel adaptive quantum multi-objective parrot optimizer (AQMOPO) for addressing the task allocation problem of searching an underwater region by a system comprising multiple unmanned underwater vehicles (UUVs). The three objectives for evaluating the effectiveness of this task allocation are the shortest moving path, the uniform task time, and the minimum number of turns in the search process, and these three objectives are competitive and incommensurable. The conventional parrot optimizer is applied to multi-objective optimization problems in accordance with the theory of non-dominated sorting. Furthermore, the solutions and parameters in AQMOPO are described in terms of quantum and updated by quantum computation to enhance the search performance of the algorithm, taking into account the coverage circles in the mission area. Subsequently, the adaptive strategy is employed to adjust the behaviors within the parrot optimizer, while the elite retention strategy is utilized to retain the most optimal solutions, thereby enhancing the efficacy of the algorithm. The results of the simulation demonstrate that the AQMOPO proposed in this paper is capable of implementing multi-objective task allocation for multi-UUV systems, thereby enabling them to complete searches in underwater complex areas.